Work

Preoperative Multiparametric MRI ‐Based Tumour–Periprostatic Adipose Tissue Interface Characterisation for Extraprostatic Extension Prediction in Prostate Cancer

Year: 2026

Type: article

Abstract: OBJECTIVE: To evaluate the independent predictive value of tumour-periprostatic adipose tissue (PPAT) interface features on preoperative multiparametric magnetic resonance imaging (mpMRI) for extraprostatic extension (EPE) in prostate cancer and to compare discrimination and clinical net benefit with a baseline clinical model. METHODS: This single-centre retrospective cohort included patients who underwent radical prostatectomy with mpMRI completed within 8 weeks. On a single axial slice at maximum tumour diameter, five simplified interface features were measured using standard PACS tools: contact length, contact angle, T2 signal intensity ratio, interface apparent diffusion coefficient (3-mm annular zone) and capsular integrity score (0-2 scale). A baseline clinical model (prostate-specific antigen [PSA], PSA density, PI-RADS and biopsy Gleason score) and a combined model (baseline variables plus LASSO-selected interface features) were constructed. Bootstrap internal validation (1000 iterations) with bias correction was performed. Discrimination was assessed using the area under the curve (AUC), and calibration curves and decision curve analysis evaluated accuracy and net clinical benefit. RESULTS: A total of 240 patients were included, with an EPE prevalence of 34.2% (82/240). The combined model achieved a bias-corrected AUC of 0.823 (95% confidence interval [CI]: 0.768-0.878), suggesting improvement over the baseline model's AUC of 0.744 (95% CI: 0.680-0.808). Decision curve analysis revealed a higher net benefit for the combined model across clinically relevant threshold probabilities (10%-50%). CONCLUSIONS: Simplified tumour-PPAT interface features independently predict EPE without increasing imaging complexity, improving discrimination and clinical value for preoperative risk stratification.

Source: Cancer Medicine

DOI: 10.1002/cam4.71613